Search

The assessment of the suitability and status of irrigation water quality from the aspect of its potential negative impact on soil salinization and mapping of spatial distribution within the area of the three Morava rivers, which includes the South, West, and Great Morava basins, was the purpose of this research. A total of 215 samples of irrigation water were tested, and their quality was evaluated based on the analysis of the following parameters: pH, electrical conductivity (EC), total dissolved salt (TDS), sodium adsorption ratio (SAR), and content of SO42−, Cl−, HCO3−, CO32−, Mg2+, Ca2+, Na+, and K+. The results showed that the average content of ions was as follows: Ca2+ > Mg2+ > Na+ > K+ and HCO3− > SO42− > Cl− > CO32−. The assessment of irrigation water suitability was determined by calculating the following indices: percentage sodium (Na %), residual sodium carbonate (RSC), permeability index (PI), magnesium hazard (MH), potential salinity (PS), Kelley’s index (KI), total hardness (TH), irrigation water quality index (IWQI). Based on Wilcox’s diagram, the USSL diagram, and the Doneen chart, it was concluded that most of the samples were suitable for irrigation. Using multivariate statistical techniques and correlation matrices in combination with other hydrogeochemical tools such as Piper’s, Chadha’s, and Gibbs diagrams, the main factors associated with hydrogeochemical variability were identified.

The principles of sustainable agriculture in the 21st century are based on the preservation of basic natural resources and environmental protection, which is achieved through a multidisciplinary approach in obtaining solutions and applying information technologies. Prediction models of bioavailability of trace elements (TEs) represent the basis for the development of machine learning and artificial intelligence in digital agriculture. Since the bioavailability of TEs is influenced by the physicochemical properties of the soil, which are characteristic of the soil type, in order to obtain more reliable prediction models in this study, the testing set from the previous study was grouped based on the soil type. The aim of this study was to examine the possibility of improvement in the prediction of bioavailability of TEs by using a different strategy of model development. After the training set was grouped based on the criteria for the new model development, the developed basic models were compared to the basic models from the previous study. The second step was to develop models based on the soil type (for the eight most common soil types in the Republic of Serbia&mdash, RS) and to compare their reliability to the basic models. From the total number of developed models by soil type (80), 75% were accepted as statistically reliable for predicting the bioavailability of TEs by soil type and 70% of prediction models had a higher determination coefficient (R2), compared to the basic models. For the Fluvisol soil type, all prediction models were accepted, while the least reliable prediction was for the Planosol type. As in the previous study of bioavailability prediction for TEs, the prediction models for Cu stood out, with more than half of the models with R2 greater than 0.90. Results of this study indicated that the formation of a testing set by soil type derives models whose predictions are more reliable than the basic ones. To improve the performance of prediction models, it is necessary to include additional physicochemical parameters and to conduct an adequate analysis of extensive testing sets with more comprehensive statistical techniques.

The bioavailability of trace elements (TEs) is one of the major factors for successful plant production and environmental protection. The aim of this study was to determine the extent to which TEs are bioavailable and which of the basic soil parameters affect bioavailability. The survey included agricultural soil samples taken from 240 locations on the territory of the Republic of Serbia, where the soil analytics were carried out. On the basis of the analyzed data the prediction models were derived based on the Freundlich model, showing the dependence between trace elements (TEs) extracted using the DTPA buffer solution in relation to the trace elements extracted using an aqua regia, the organic matter content (SOM), the clay fractions content, and soil pH. On one part of the samples, the prediction models were separated on the basis of a suspension for determining the pH in H2O and 1M KCl. The model was applied for the following TEs: Mn, Ni, Pb, Zn, and Cu. The content of the pseudo total forms of TEs statistically significantly influenced the bioavailability of TEs in all prediction models for all studied elements. The pH value statistically significantly affected the bioavailability of Ni, Mn, Pb, and Cu also in all prediction models. The impact of SOM and clay varied depending on the model and TEs. Multiple linear regression showed that the prediction models for TE Cu (R2 = 0.763&ndash, 0.848) were the most reliable and that the bioavailability of Cu was significantly influenced by all the studied soil parameters except clay. Reliable results were also shown by the prediction models for TE Pb, but the values of the determination coefficient and investigated parameters that influenced the bioavailability varied depending on the model. The derived models for TE Mn, Zn, and Ni were less reliable (R2 is approximately 50% or less), and the effect of the tested parameters on bioavailability varied depending on the model and TEs.